{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [向量化的字符串操作](03.10-Working-With-Strings.ipynb) | [目录](Index.ipynb) | [高性能Pandas: eval() 和 query()](03.12-Performance-Eval-and-Query.ipynb) >\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/wangyingsm/Python-Data-Science-Handbook/blob/master/notebooks/03.11-Working-with-Time-Series.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working with Time Series\n",
    "\n",
    "# 在时间序列上操作"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Pandas was developed in the context of financial modeling, so as you might expect, it contains a fairly extensive set of tools for working with dates, times, and time-indexed data.\n",
    "Date and time data comes in a few flavors, which we will discuss here:\n",
    "\n",
    "> - *Time stamps* reference particular moments in time (e.g., July 4th, 2015 at 7:00am).\n",
    "> - *Time intervals* and *periods* reference a length of time between a particular beginning and end point; for example, the year 2015. Periods usually reference a special case of time intervals in which each interval is of uniform length and does not overlap (e.g., 24 hour-long periods comprising days).\n",
    "> - *Time deltas* or *durations* reference an exact length of time (e.g., a duration of 22.56 seconds).\n",
    "\n",
    "Pandas的发展过程具有很强的金融领域背景，因此你可以预料的是，它一定包括一整套工具用于处理日期、时间和时间索引数据。日期和时间数据有如下几类来源，我们会在本节中进行讨论：\n",
    "\n",
    "- *时间戳* 代表着一个特定的时间点（例如2015年7月4日上午7点）。\n",
    "- *时间间隔*和*周期* 代表着从开始时间点到结束时间点之间的时间单位长度；例如2015一整年。周期通常代表一段特殊的时间间隔，每个时间间隔的长度都是统一的，彼此之间不重叠（例如一天由24个小时组成）。\n",
    "- *时间差*或*持续时间*代表这一段准确的时间长度（例如22.56秒持续时间）。\n",
    "\n",
    "> In this section, we will introduce how to work with each of these types of date/time data in Pandas.\n",
    "This short section is by no means a complete guide to the time series tools available in Python or Pandas, but instead is intended as a broad overview of how you as a user should approach working with time series.\n",
    "We will start with a brief discussion of tools for dealing with dates and times in Python, before moving more specifically to a discussion of the tools provided by Pandas.\n",
    "After listing some resources that go into more depth, we will review some short examples of working with time series data in Pandas.\n",
    "\n",
    "在本节中，我们将介绍在Pandas中如何使用上述的这些时间类型数据。这个简短的小节不可能覆盖Python或Pandas中所有时间序列工具的内容，但可以作为引导你入门使用它们的一个概述。我们首先简要介绍一些在Python当中处理日期时间的工具，然后再进入到Pandas提供的相应工具的详细介绍上。在列出更加深入的学习资源之后，我们还会使用一些简短的例子在说明在Pandas中怎样处理时间序列数据。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dates and Times in Python\n",
    "\n",
    "## Python中的日期和时间\n",
    "\n",
    "> The Python world has a number of available representations of dates, times, deltas, and timespans.\n",
    "While the time series tools provided by Pandas tend to be the most useful for data science applications, it is helpful to see their relationship to other packages used in Python.\n",
    "\n",
    "Python本身就带有很多有关日期、时间、时间差和间隔的表示方法。Pandas提供的时间序列工具在数据科学领域会更加的强大，但是首先学习相关的Python的工具包会对我们理解它们更加有帮助。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Native Python dates and times: ``datetime`` and ``dateutil``\n",
    "\n",
    "### 原生Python日期和时间：`datetime` 和 `dateutil`\n",
    "\n",
    "> Python's basic objects for working with dates and times reside in the built-in ``datetime`` module.\n",
    "Along with the third-party ``dateutil`` module, you can use it to quickly perform a host of useful functionalities on dates and times.\n",
    "For example, you can manually build a date using the ``datetime`` type:\n",
    "\n",
    "Python最基础的日期和时间处理包就是`datetime`。如果加上第三方的`dateutil`模块，你就能迅速的对日期和时间进行许多有用的操作了。例如，你可以手动创建一个`datetime`对象："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2015, 7, 4, 0, 0)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "datetime(year=2015, month=7, day=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Or, using the ``dateutil`` module, you can parse dates from a variety of string formats:\n",
    "\n",
    "或者使用`dateutil`模块，你可以从许多不同的字符串格式中解析出`datetime`对象："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2015, 7, 4, 0, 0)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dateutil import parser\n",
    "date = parser.parse(\"4th of July, 2015\")\n",
    "date"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Once you have a ``datetime`` object, you can do things like printing the day of the week:\n",
    "\n",
    "获得`datetime`对象之后，你可以对它进行很多操作，包括输出这天是星期几："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Saturday'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date.strftime('%A')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> In the final line, we've used one of the standard string format codes for printing dates (``\"%A\"``), which you can read about in the [strftime section](https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior) of Python's [datetime documentation](https://docs.python.org/3/library/datetime.html).\n",
    "Documentation of other useful date utilities can be found in [dateutil's online documentation](http://labix.org/python-dateutil).\n",
    "A related package to be aware of is [``pytz``](http://pytz.sourceforge.net/), which contains tools for working with the most migrane-inducing piece of time series data: time zones.\n",
    "\n",
    "在上面的代码中，我们使用了标准的字符串格式化编码来打印日期（`\"%A\"`），你可以在[时间格式化](https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior)在线文档中看到全部的说明。Python的`datetime`在线文档可以参考[datetime文档](https://docs.python.org/3/library/datetime.html)。其他很有用的日期时间工具`dateutil`的文档可在[dateutil在线文档](http://labix.org/python-dateutil)找到。还有一个值得注意的第三方包是[`pytz`](http://pytz.sourceforge.net/)，用来处理最头痛的时间序列数据：时区。\n",
    "\n",
    "> The power of ``datetime`` and ``dateutil`` lie in their flexibility and easy syntax: you can use these objects and their built-in methods to easily perform nearly any operation you might be interested in.\n",
    "Where they break down is when you wish to work with large arrays of dates and times:\n",
    "just as lists of Python numerical variables are suboptimal compared to NumPy-style typed numerical arrays, lists of Python datetime objects are suboptimal compared to typed arrays of encoded dates.\n",
    "\n",
    "`datetime`和`dateutil`的强大在于它们灵活而易懂的语法：你可以使用这些对象內建的方法就可以完成几乎所有你感兴趣的时间操作。但是当对付大量的日期时间组成的数组时，它们就无法胜任了：就像Python的列表和NumPy的类型数组对比一样，Python的日期时间对象在这种情况下就无法与编码后的日期时间数组比较了。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Typed arrays of times: NumPy's ``datetime64``\n",
    "\n",
    "### 时间的类型数组：NumPy 的 `datetime64`\n",
    "\n",
    "> The weaknesses of Python's datetime format inspired the NumPy team to add a set of native time series data type to NumPy.\n",
    "The ``datetime64`` dtype encodes dates as 64-bit integers, and thus allows arrays of dates to be represented very compactly.\n",
    "The ``datetime64`` requires a very specific input format:\n",
    "\n",
    "Python日期时间对象的弱点促使NumPy的开发团队在NumPy中加入了优化的时间序列数据类型。`datetime64`数据类型将日期时间编码成了一个64位的整数，因此NumPy存储日期时间的格式非常紧凑。`datetime64`规定了非常明确的输入格式："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array('2015-07-04', dtype='datetime64[D]')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "date = np.array('2015-07-04', dtype=np.datetime64)\n",
    "date"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Once we have this date formatted, however, we can quickly do vectorized operations on it:\n",
    "\n",
    "然后我们就能立刻在这个日期数组之上应用向量化操作："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2015-07-04', '2015-07-05', '2015-07-06', '2015-07-07',\n",
       "       '2015-07-08', '2015-07-09', '2015-07-10', '2015-07-11',\n",
       "       '2015-07-12', '2015-07-13', '2015-07-14', '2015-07-15'],\n",
       "      dtype='datetime64[D]')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date + np.arange(12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Because of the uniform type in NumPy ``datetime64`` arrays, this type of operation can be accomplished much more quickly than if we were working directly with Python's ``datetime`` objects, especially as arrays get large\n",
    "(we introduced this type of vectorization in [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb)).\n",
    "\n",
    "因为NumPy数组中所有元素都具有统一的`datetime64`类型，上面的向量化操作将会比我们使用Python的`datetime`对象高效许多，特别是当数组变得很大的情况下（我们在[使用Numpy计算：通用函数](02.03-Computation-on-arrays-ufuncs.ipynb)中详细介绍过）。\n",
    "\n",
    "> One detail of the ``datetime64`` and ``timedelta64`` objects is that they are built on a *fundamental time unit*.\n",
    "Because the ``datetime64`` object is limited to 64-bit precision, the range of encodable times is $2^{64}$ times this fundamental unit.\n",
    "In other words, ``datetime64`` imposes a trade-off between *time resolution* and *maximum time span*.\n",
    "\n",
    "关于`datetime64`和`timedelta64`对象还有一个细节就是它们都是在*基本时间单位*之上构建的。因为`datetime64`被限制在64位精度上，因此它可被编码的时间范围就是$2^{64}$乘以相应的时间单位。换言之，`datetime64`需要在*时间精度*和*最大时间间隔*之间进行取舍。\n",
    "\n",
    "> For example, if you want a time resolution of one nanosecond, you only have enough information to encode a range of $2^{64}$ nanoseconds, or just under 600 years.\n",
    "NumPy will infer the desired unit from the input; for example, here is a day-based datetime:\n",
    "\n",
    "例如，如果时间单位是纳秒，`datetime64`类型能够编码的时间范围就是$2^{64}$纳秒，不到600年。NumPy可以自动从输入推断需要的时间精度（单位）；如下面是天为单位："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.datetime64('2015-07-04')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.datetime64('2015-07-04')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Here is a minute-based datetime:\n",
    "\n",
    "下面是分钟为单位："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.datetime64('2015-07-04T12:00')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.datetime64('2015-07-04 12:00')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Notice that the time zone is automatically set to the local time on the computer executing the code.\n",
    "You can force any desired fundamental unit using one of many format codes; for example, here we'll force a nanosecond-based time:\n",
    "\n",
    "还需要注意的是，日期时间会自动按照本地计算机的时间来进行设置。你可以通过额外指定时间单位参数来设置你需要的精度；例如，下面使用的是纳秒单位："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.datetime64('2015-07-04T12:59:59.500000000')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.datetime64('2015-07-04 12:59:59.50', 'ns')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The following table, drawn from the [NumPy datetime64 documentation](http://docs.scipy.org/doc/numpy/reference/arrays.datetime.html), lists the available format codes along with the relative and absolute timespans that they can encode:\n",
    "\n",
    "下面这张表，来自[NumPy datetime64类型在线文档](http://docs.scipy.org/doc/numpy/reference/arrays.datetime.html)，列出了可用的时间单位代码以及其相应的时间范围限制："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|代码    | 含义     | 时间范围 (相对) | 时间范围 (绝对)   |\n",
    "|--------|-------------|----------------------|------------------------|\n",
    "| ``Y``  | 年\t       | ± 9.2e18 年       | [公元前9.2e18 至 公元后9.2e18] |\n",
    "| ``M``  | 月       | ± 7.6e17 年       | [公元前7.6e17 至 公元后7.6e17] |\n",
    "| ``W``  | 星期\t       | ± 1.7e17 年       | [公元前1.7e17 至 公元后1.7e17] |\n",
    "| ``D``  | 日         | ± 2.5e16 年       | [公元前2.5e16 至 公元后2.5e16] |\n",
    "| ``h``  | 小时        | ± 1.0e15 年       | [公元前1.0e15 至 公元后1.0e15] |\n",
    "| ``m``  | 分钟      | ± 1.7e13 年       | [公元前1.7e13 至 公元后1.7e13] |\n",
    "| ``s``  | 秒      | ± 2.9e12 年       | [公元前2.9e9 至 公元后2.9e9]  |\n",
    "| ``ms`` | 毫秒 | ± 2.9e9 年        | [公元前2.9e6 至 公元后2.9e6]  |\n",
    "| ``us`` | 微秒 | ± 2.9e6 年        | [公元前290301 至 公元后294241] |\n",
    "| ``ns`` | 纳秒  | ± 292 年          | [公元后1678 至 公元后2262]    |\n",
    "| ``ps`` | 皮秒  | ± 106 天           | [公元后1969 至 公元后1970]    |\n",
    "| ``fs`` | 飞秒 | ± 2.6 小时          | [公元后1969 至 公元后1970]    |\n",
    "| ``as`` | 阿秒  | ± 9.2 秒        | [公元后1969 至 公元后1970]    |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> For the types of data we see in the real world, a useful default is ``datetime64[ns]``, as it can encode a useful range of modern dates with a suitably fine precision.\n",
    "\n",
    "对于我们目前真实世界的数据来说，一个合适的默认值可以是`datetime64[ns]`，因为它既能包含现代的时间范围，也能提供相当高的时间精度。\n",
    "\n",
    "> Finally, we will note that while the ``datetime64`` data type addresses some of the deficiencies of the built-in Python ``datetime`` type, it lacks many of the convenient methods and functions provided by ``datetime`` and especially ``dateutil``.\n",
    "More information can be found in [NumPy's datetime64 documentation](http://docs.scipy.org/doc/numpy/reference/arrays.datetime.html).\n",
    "\n",
    "最后，还要提醒的是，虽然`datetime64`数据类型解决了Python內建`datetime`类型的低效问题，但是它却缺少很多`datetime`特别是`dateutil`对象提供的很方便的方法。你可以在[NumPy的datetime64在线文档](http://docs.scipy.org/doc/numpy/reference/arrays.datetime.html)中查阅更多相关内容。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dates and times in pandas: best of both worlds\n",
    "\n",
    "### Pandas中的日期和时间：兼得所长\n",
    "\n",
    "> Pandas builds upon all the tools just discussed to provide a ``Timestamp`` object, which combines the ease-of-use of ``datetime`` and ``dateutil`` with the efficient storage and vectorized interface of ``numpy.datetime64``.\n",
    "From a group of these ``Timestamp`` objects, Pandas can construct a ``DatetimeIndex`` that can be used to index data in a ``Series`` or ``DataFrame``; we'll see many examples of this below.\n",
    "\n",
    "Pandas在刚才介绍的那些工具的基础上构建了`Timestamp`对象，既包含了`datetime`和`dateutil`的简单易用，又吸收了`numpy.datetime64`的高效和向量化操作优点。将这些`Timestamp`对象组合起来之后，Pandas就能构建一个`DatetimeIndex`，能在`Series`或`DataFrame`当中对数据进行索引查找；我们下面会看到很多有关的例子。\n",
    "\n",
    "> For example, we can use Pandas tools to repeat the demonstration from above.\n",
    "We can parse a flexibly formatted string date, and use format codes to output the day of the week:\n",
    "\n",
    "例如，我们使用Pandas工具可以重复上面的例子。我们可以将一个灵活表示时间的字符串解析成日期时间对象，然后用时间格式化代码进行格式化输出星期几："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2015-07-04 00:00:00')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "date = pd.to_datetime(\"4th of July, 2015\")\n",
    "date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Saturday'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date.strftime('%A')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Additionally, we can do NumPy-style vectorized operations directly on this same object:\n",
    "\n",
    "并且，我们可以将NumPy风格的向量化操作直接应用在同一个对象上："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-07-04', '2015-07-05', '2015-07-06', '2015-07-07',\n",
       "               '2015-07-08', '2015-07-09', '2015-07-10', '2015-07-11',\n",
       "               '2015-07-12', '2015-07-13', '2015-07-14', '2015-07-15'],\n",
       "              dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date + pd.to_timedelta(np.arange(12), 'D')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> In the next section, we will take a closer look at manipulating time series data with the tools provided by Pandas.\n",
    "\n",
    "下面，我们将详细介绍使用Pandas提供的工具对时间序列进行操作的方法。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas Time Series: Indexing by Time\n",
    "\n",
    "## Pandas时间序列：使用时间索引\n",
    "\n",
    "> Where the Pandas time series tools really become useful is when you begin to *index data by timestamps*.\n",
    "For example, we can construct a ``Series`` object that has time indexed data:\n",
    "\n",
    "对于Pandas时间序列工具来说，*使用时间戳来索引数据*，才是真正吸引人的地方。例如，我们可以创建一个`Series`对象具有时间索引标签："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014-07-04    0\n",
       "2014-08-04    1\n",
       "2015-07-04    2\n",
       "2015-08-04    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = pd.DatetimeIndex(['2014-07-04', '2014-08-04',\n",
    "                          '2015-07-04', '2015-08-04'])\n",
    "data = pd.Series([0, 1, 2, 3], index=index)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Now that we have this data in a ``Series``, we can make use of any of the ``Series`` indexing patterns we discussed in previous sections, passing values that can be coerced into dates:\n",
    "\n",
    "这样我们就有了一个`Series`数据，我们可以将任何`Series`索引的方法应用到这个对象上，我们可以传入参数值，Pandas会自动转换为日期时间进行操作："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014-07-04    0\n",
       "2014-08-04    1\n",
       "2015-07-04    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['2014-07-04':'2015-07-04']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> There are additional special date-only indexing operations, such as passing a year to obtain a slice of all data from that year:\n",
    "\n",
    "还有很多有关日期的索引方式，如下面将年作为参数传入，会得到一个全年数据的切片："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015-07-04    2\n",
       "2015-08-04    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['2015']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Later, we will see additional examples of the convenience of dates-as-indices.\n",
    "But first, a closer look at the available time series data structures.\n",
    "\n",
    "后面我们会看到更多使用日期时间作为索引值的例子。首先来详细看看时间序列数据的结构。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas Time Series Data Structures\n",
    "\n",
    "## Pandas时间序列数据结构\n",
    "\n",
    "> This section will introduce the fundamental Pandas data structures for working with time series data:\n",
    "\n",
    "> - For *time stamps*, Pandas provides the ``Timestamp`` type. As mentioned before, it is essentially a replacement for Python's native ``datetime``, but is based on the more efficient ``numpy.datetime64`` data type. The associated Index structure is ``DatetimeIndex``.\n",
    "> - For *time Periods*, Pandas provides the ``Period`` type. This encodes a fixed-frequency interval based on ``numpy.datetime64``. The associated index structure is ``PeriodIndex``.\n",
    "> - For *time deltas* or *durations*, Pandas provides the ``Timedelta`` type. ``Timedelta`` is a more efficient replacement for Python's native ``datetime.timedelta`` type, and is based on ``numpy.timedelta64``. The associated index structure is ``TimedeltaIndex``.\n",
    "\n",
    "这部分内容会介绍Pandas在处理时间序列数据时候使用的基本数据结构：\n",
    "\n",
    "- 对于*时间戳*，Pandas提供了`Timestamp`类型。正如上面所述，它可以作为Python原生`datetime`类型的替代，但是它是构建在`numpy.datetime64`数据类型之上的。对应的索引结构是`DatetimeIndex`。\n",
    "- 对于*时间周期*，Pandas提供了`Period`类型。它是在`numpy.datetime64`的基础上编码了一个固定周期间隔的时间。对应的索引结构是`PeriodIndex`。\n",
    "- 对于*时间差*或*持续时间*，Pandas提供了`Timedelta`类型。构建于`numpy.timedelta64`之上，是Python原生`datetime.timedelta`类型的高性能替代。对应的索引结构是`TimedeltaIndex`。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The most fundamental of these date/time objects are the ``Timestamp`` and ``DatetimeIndex`` objects.\n",
    "While these class objects can be invoked directly, it is more common to use the ``pd.to_datetime()`` function, which can parse a wide variety of formats.\n",
    "Passing a single date to ``pd.to_datetime()`` yields a ``Timestamp``; passing a series of dates by default yields a ``DatetimeIndex``:\n",
    "\n",
    "上述这些日期时间对象中最基础的是`Timestamp`和`DatetimeIndex`对象。虽然这些对象可以直接被创建，但是更通用的做法是使用`pd.to_datetime()`函数，该函数可以将多种格式的字符串解析成日期时间。将一个日期时间传递给`pd.to_datetime()`会得到一个`Timestamp`对象；将一系列的日期时间传递过去会得到一个`DatetimeIndex`对象："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-07-03', '2015-07-04', '2015-07-06', '2015-07-07',\n",
       "               '2015-07-08'],\n",
       "              dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.to_datetime([datetime(2015, 7, 3), '4th of July, 2015',\n",
    "                       '2015-Jul-6', '07-07-2015', '20150708'])\n",
    "dates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Any ``DatetimeIndex`` can be converted to a ``PeriodIndex`` with the ``to_period()`` function with the addition of a frequency code; here we'll use ``'D'`` to indicate daily frequency:\n",
    "\n",
    "任何`DatetimeIndex`对象都能使用`to_period()`函数转换成`PeriodIndex`对象，不过需要额外指定一个频率的参数码；下面我们使用`'D'`来指定频率为天："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2015-07-03', '2015-07-04', '2015-07-06', '2015-07-07',\n",
       "             '2015-07-08'],\n",
       "            dtype='period[D]', freq='D')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates.to_period('D')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> A ``TimedeltaIndex`` is created, for example, when a date is subtracted from another:\n",
    "\n",
    "`TimedeltaIndex`对象可以通过日期时间相减来创建，例如："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TimedeltaIndex(['0 days', '1 days', '3 days', '4 days', '5 days'], dtype='timedelta64[ns]', freq=None)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates - dates[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regular sequences: ``pd.date_range()``\n",
    "\n",
    "### 规则序列：`pd.date_range()`\n",
    "\n",
    "> To make the creation of regular date sequences more convenient, Pandas offers a few functions for this purpose: ``pd.date_range()`` for timestamps, ``pd.period_range()`` for periods, and ``pd.timedelta_range()`` for time deltas.\n",
    "We've seen that Python's ``range()`` and NumPy's ``np.arange()`` turn a startpoint, endpoint, and optional stepsize into a sequence.\n",
    "Similarly, ``pd.date_range()`` accepts a start date, an end date, and an optional frequency code to create a regular sequence of dates.\n",
    "By default, the frequency is one day:\n",
    "\n",
    "Pandas提供了三个函数来创建规则的日期时间序列，`pd.date_range()`来创建时间戳的序列，`pd.period_range()`来创建周期的序列，`pd.timedelta_range()`来创建时间差的序列。我们都已经学习过Python的`range()`和NumPy的`arange()`了，它们接受开始点、结束点和可选的步长参数来创建序列。同样，`pd.date_range()`接受开始日期时间、结束日期时间和可选的周期码来创建日期时间的规则序列。默认周期为一天："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-07-03', '2015-07-04', '2015-07-05', '2015-07-06',\n",
       "               '2015-07-07', '2015-07-08', '2015-07-09', '2015-07-10'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range('2015-07-03', '2015-07-10')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Alternatively, the date range can be specified not with a start and endpoint, but with a startpoint and a number of periods:\n",
    "\n",
    "而且，日期时间的范围不仅能通过结束日期时间指定，还能通过开始日期时间和一个持续值来指定："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-07-03', '2015-07-04', '2015-07-05', '2015-07-06',\n",
       "               '2015-07-07', '2015-07-08', '2015-07-09', '2015-07-10'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range('2015-07-03', periods=8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The spacing can be modified by altering the ``freq`` argument, which defaults to ``D``.\n",
    "For example, here we will construct a range of hourly timestamps:\n",
    "\n",
    "日期时间的间隔可以通过指定`freq`频率参数来修改，否则默认为天`D`。例如，下面创建一段以小时为间隔单位的时间范围："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-07-03 00:00:00', '2015-07-03 01:00:00',\n",
       "               '2015-07-03 02:00:00', '2015-07-03 03:00:00',\n",
       "               '2015-07-03 04:00:00', '2015-07-03 05:00:00',\n",
       "               '2015-07-03 06:00:00', '2015-07-03 07:00:00'],\n",
       "              dtype='datetime64[ns]', freq='H')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range('2015-07-03', periods=8, freq='H')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> To create regular sequences of ``Period`` or ``Timedelta`` values, the very similar ``pd.period_range()`` and ``pd.timedelta_range()`` functions are useful.\n",
    "Here are some monthly periods:\n",
    "\n",
    "要创建`Period`或`Timedelta`对象，可以类似的调用`pd.period_range()`和`pd.timedelta_range()`函数。下面是以月为单位的时间周期序列："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2015-07', '2015-08', '2015-09', '2015-10', '2015-11', '2015-12',\n",
       "             '2016-01', '2016-02'],\n",
       "            dtype='period[M]', freq='M')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.period_range('2015-07', periods=8, freq='M')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> And a sequence of durations increasing by an hour:\n",
    "\n",
    "下面是以小时为单位的持续时间序列："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TimedeltaIndex(['00:00:00', '01:00:00', '02:00:00', '03:00:00', '04:00:00',\n",
       "                '05:00:00', '06:00:00', '07:00:00', '08:00:00', '09:00:00'],\n",
       "               dtype='timedelta64[ns]', freq='H')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.timedelta_range(0, periods=10, freq='H')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> All of these require an understanding of Pandas frequency codes, which we'll summarize in the next section.\n",
    "\n",
    "上述函数都需要我们理解Pandas的频率编码，我们马上会介绍它。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Frequencies and Offsets\n",
    "\n",
    "## 频率和偏移值\n",
    "\n",
    "> Fundamental to these Pandas time series tools is the concept of a frequency or date offset.\n",
    "Just as we saw the ``D`` (day) and ``H`` (hour) codes above, we can use such codes to specify any desired frequency spacing.\n",
    "The following table summarizes the main codes available:\n",
    "\n",
    "要使用Pandas时间序列工具，我们需要理解频率和时间偏移值的概念。就像前面我们看到的`D`代表天和`H`代表小时一样，我们可以使用这类符号码指定需要的频率间隔。下表总结了主要的频率码："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| 码   | 说明         | 码   | 说明          |\n",
    "|--------|---------------------|--------|----------------------|\n",
    "| ``D``  | 自然日        | ``B``  | 工作日        |\n",
    "| ``W``  | 周              |        |                      |\n",
    "| ``M``  | 自然日月末           | ``BM`` | 工作日月末   |\n",
    "| ``Q``  | 自然日季末         | ``BQ`` | 工作日季末 |\n",
    "| ``A``  | 自然日年末            | ``BA`` | 工作日年末    |\n",
    "| ``H``  | 自然小时               | ``BH`` | 工作小时       |\n",
    "| ``T``  | 分钟             |        |                      |\n",
    "| ``S``  | 秒             |        |                      |\n",
    "| ``L``  | 毫秒         |        |                      |\n",
    "| ``U``  | 微秒        |        |                      |\n",
    "| ``N``  | 纳秒         |        |                      |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The monthly, quarterly, and annual frequencies are all marked at the end of the specified period.\n",
    "By adding an ``S`` suffix to any of these, they instead will be marked at the beginning:\n",
    "\n",
    "上面的月、季度和年都代表着该时间周期的结束时间。如果在这些码后面加上`S`后缀，则代表这些时间周期的起始时间："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| 码    | 说明            || 码    | 说明            |\n",
    "|---------|------------------------||---------|------------------------|\n",
    "| ``MS``  | 自然日月初            ||``BMS``  | 工作日月初   |\n",
    "| ``QS``  | 自然日季初          ||``BQS``  | 工作日季初 |\n",
    "| ``AS``  | 自然日年初             ||``BAS``  | 工作日年初    |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Additionally, you can change the month used to mark any quarterly or annual code by adding a three-letter month code as a suffix:\n",
    "\n",
    "> - ``Q-JAN``, ``BQ-FEB``, ``QS-MAR``, ``BQS-APR``, etc.\n",
    "> - ``A-JAN``, ``BA-FEB``, ``AS-MAR``, ``BAS-APR``, etc.\n",
    "\n",
    "并且你可以通过在季度或者年的符号码后面添加三个字母的月份缩写来指定周期进行分隔的月份：\n",
    "\n",
    "- ``Q-JAN``、``BQ-FEB``、``QS-MAR``、``BQS-APR``等\n",
    "- ``A-JAN``、``BA-FEB``、``AS-MAR``、``BAS-APR``等\n",
    "\n",
    "> In the same way, the split-point of the weekly frequency can be modified by adding a three-letter weekday code:\n",
    "\n",
    "> - ``W-SUN``, ``W-MON``, ``W-TUE``, ``W-WED``, etc.\n",
    "\n",
    "同样，每周的分隔日也可以通过在周符号码后面添加三个字母的星期几缩写来指定：\n",
    "\n",
    "- ``W-SUN``、``W-MON``、``W-TUE``、``W-WED``等\n",
    "\n",
    "> On top of this, codes can be combined with numbers to specify other frequencies.\n",
    "For example, for a frequency of 2 hours 30 minutes, we can combine the hour (``H``) and minute (``T``) codes as follows:\n",
    "\n",
    "在此之上，符号码还可以进行组合用来代表其他的频率。例如要表示2小时30分钟的频率，我们可以通过将小时（`H`）和分钟（`T`）的符号码进行组合得到："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TimedeltaIndex(['00:00:00', '02:30:00', '05:00:00', '07:30:00', '10:00:00',\n",
       "                '12:30:00', '15:00:00', '17:30:00', '20:00:00'],\n",
       "               dtype='timedelta64[ns]', freq='150T')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.timedelta_range(0, periods=9, freq=\"2H30T\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> All of these short codes refer to specific instances of Pandas time series offsets, which can be found in the ``pd.tseries.offsets`` module.\n",
    "For example, we can create a business day offset directly as follows:\n",
    "\n",
    "上述的这些短的符号码实际上是Pandas时间序列偏移值的对象实例的别名，你可以在`pd.tseries.offsets`模块中找到这些偏移值实例。例如，我们也可以通过一个偏移值对象实例来创建时间序列："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-07-01', '2015-07-02', '2015-07-03', '2015-07-06',\n",
       "               '2015-07-07'],\n",
       "              dtype='datetime64[ns]', freq='B')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas.tseries.offsets import BDay\n",
    "pd.date_range('2015-07-01', periods=5, freq=BDay())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> For more discussion of the use of frequencies and offsets, see the [\"DateOffset\" section](http://pandas.pydata.org/pandas-docs/stable/timeseries.html#dateoffset-objects) of the Pandas documentation.\n",
    "\n",
    "更多有关频率和偏移值的讨论，请参阅Pandas在线文档[日期时间偏移值章节](http://pandas.pydata.org/pandas-docs/stable/timeseries.html#dateoffset-objects)。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resampling, Shifting, and Windowing\n",
    "\n",
    "## 重新取样、移动和窗口\n",
    "\n",
    "> The ability to use dates and times as indices to intuitively organize and access data is an important piece of the Pandas time series tools.\n",
    "The benefits of indexed data in general (automatic alignment during operations, intuitive data slicing and access, etc.) still apply, and Pandas provides several additional time series-specific operations.\n",
    "\n",
    "使用日期和时间作为索引来直观的组织和访问数据的能力，是Pandas时间序列工具的重要功能。前面介绍过的索引的那些通用优点（自动对齐，直观的数据切片和访问等）依然有效，而且Pandas提供了许多额外的时间序列相关操作。\n",
    "\n",
    "> We will take a look at a few of those here, using some stock price data as an example.\n",
    "Because Pandas was developed largely in a finance context, it includes some very specific tools for financial data.\n",
    "For example, the accompanying ``pandas-datareader`` package (installable via ``conda install pandas-datareader``), knows how to import financial data from a number of available sources, including Yahoo finance, Google Finance, and others.\n",
    "Here we will load Google's closing price history:\n",
    "\n",
    "我们会在这里介绍其中的一些，使用股票价格数据作为例子。因为Pandas是在金融背景基础上发展而来的，因此它具有一些特别的金融数据相关工具。例如，`pandas-datareader`包（可以通过`conda install pandas-datareader`进行安装）可以被用来从许多可用的数据源导入金融数据，包括Yahoo金融，Google金融和其他。下面我们将载入Google的收市价历史数据：\n",
    "\n",
    "译者注：在新版的`pandas-datareader`中，数据源`google`已经不被支持，因此，译者采用了`yahoo`数据源。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-08-19</th>\n",
       "      <td>51.835709</td>\n",
       "      <td>47.800831</td>\n",
       "      <td>49.813286</td>\n",
       "      <td>49.982655</td>\n",
       "      <td>44871300.0</td>\n",
       "      <td>49.982655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-20</th>\n",
       "      <td>54.336334</td>\n",
       "      <td>50.062355</td>\n",
       "      <td>50.316402</td>\n",
       "      <td>53.952770</td>\n",
       "      <td>22942800.0</td>\n",
       "      <td>53.952770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-23</th>\n",
       "      <td>56.528118</td>\n",
       "      <td>54.321388</td>\n",
       "      <td>55.168217</td>\n",
       "      <td>54.495735</td>\n",
       "      <td>18342800.0</td>\n",
       "      <td>54.495735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-24</th>\n",
       "      <td>55.591629</td>\n",
       "      <td>51.591621</td>\n",
       "      <td>55.412300</td>\n",
       "      <td>52.239193</td>\n",
       "      <td>15319700.0</td>\n",
       "      <td>52.239193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-25</th>\n",
       "      <td>53.798351</td>\n",
       "      <td>51.746044</td>\n",
       "      <td>52.284027</td>\n",
       "      <td>52.802086</td>\n",
       "      <td>9232100.0</td>\n",
       "      <td>52.802086</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 High        Low       Open      Close      Volume  Adj Close\n",
       "Date                                                                         \n",
       "2004-08-19  51.835709  47.800831  49.813286  49.982655  44871300.0  49.982655\n",
       "2004-08-20  54.336334  50.062355  50.316402  53.952770  22942800.0  53.952770\n",
       "2004-08-23  56.528118  54.321388  55.168217  54.495735  18342800.0  54.495735\n",
       "2004-08-24  55.591629  51.591621  55.412300  52.239193  15319700.0  52.239193\n",
       "2004-08-25  53.798351  51.746044  52.284027  52.802086   9232100.0  52.802086"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas_datareader import data\n",
    "\n",
    "goog = data.DataReader('GOOG', start='2004', end='2016',\n",
    "                       data_source='yahoo')\n",
    "goog.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> For simplicity, we'll use just the closing price:\n",
    "\n",
    "为简单起见，我们仅使用收市价："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "goog = goog['Close']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> We can visualize this using the ``plot()`` method, after the normal Matplotlib setup boilerplate (see [Chapter 4](04.00-Introduction-To-Matplotlib.ipynb)):\n",
    "\n",
    "我们可以使用`plot()`方法来做出图表，当然之前要先完成Matplotlib的相关初始化工作（参见[第四章](04.00-Introduction-To-Matplotlib.ipynb)）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn; seaborn.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MPsApIt2aKFNKqYjyTXOQlZHCj48Z2OzXSE1OjPhNUYBJh3WP+Gu2RLg9+6nGmG0ikgI8CDwKvNF61bJkZ7fsBktbyc2NbFa6aKZtjU2x0lZHkhXKZvxwBOecEHgOe3u1NS8vs12+b0NhBXtjzDb7a5WIPAa8DTwEeJefiUgO4DbGFIlIfrCy5lSusLA0ZI7o9pSbm0FBQUl7V6NNaFtjUyy1tfCANXfE6XYHbFNbtvXc44bQt3s697+8HKBNf8ZOpyNoJ7nJYC8inYFEY8wB+8brz4DlwFKgk4gcY4/NXw28Yl8WqkwppSLKszVgZHdtbZlTJvYD4J4rJlIXRZ3VcHr23YHXRSQBSADWANcaY1wicjEwW0RSsadXAoQqU0qpiLNjalOrZdtSz+zwcuC0lSaDvTFmMzA2SNlXwKjmlimlVCR5bs9GUayPOpobRynV8TVj0VK80mCvlOrwPKG+qbTE8UyDvVKqw/N27DXWB6XBXinVISzbUIDJ3x+wzJPWWGN9cBrslVJRZ/veUtZvK/Y+r61z8cjrK7n3xWUhr4um2TjRRoO9Uirq3PX0Yma+8C1V1dYG4KUVVu4bN/D5dzsbLbZ0eadetmUtOxbNZ6+UilqrtxRRUVVL/p5S77Fn3l9HWWUtp0zsxxNvrWLx2r3cfkHA2eHKh/bslVJRxzOrZs/+cua8u5aPv9nmV/7RknxKK2pYvNbaImPuR+sB+MYUtG1FOxAN9kqpqNMl3dql6dXPGu/6BFBcWs3W3fU5Z3bsKwMgtRXywMcKDfZKqahSUVXL/pKqgGXD+9XvKTtv+Y5G5adO6tdq9erodMxeKRVVlm/cF/D4LeeNYcSALBat2cOT76xhqSmgU0oiFVW13nNyunRqq2p2ONqzV0pFlT1F5QCM6J/ld7xPXjpOh4P0TkneYzW1Lmb/err3eVKihrRgtGevlIoqS9dbN1nPOnYQL368nh9O6s94yfOWZ2emeh/X1rk0wIdJg71SKqqMGpTNjoIyBvXM5Pc/n9CovFdOZ37xoxHMeXet99hVZxxGj27hbzAejzTYK6XazYLvdvLs++u46dwjGDUoG4CS8mqyMlJCroadMqonCQkOjhicA8DEkdGxz2s0a1awF5E/AHcDo4wxq0RkEjAb6IS9QYkxZq99btAypZQC+GyZNaPmoVdX8NTtxwFg8otDXeI1aWSPVqtXLAp7sEtExgGTgHz7uQOYC1xnjBkGLABmNlWmlFIeY4dYPfPcrPpZNPsOVAadeqlaLqxgLyIpwCzgWupTRx8JVNp7zAI8AZwbRplSSlFb5/IuhtpTVM5lMz/lhY+tlbC9cqJrS79YEG7P/k/AXGPM9z7H+gFbPU+MMfsAp4h0a6JMKaV49P9WsmSd/8juJ0u3A9Cve3p7VCmmNTlmLyJHAxOAO1q/Ov6ys6P7F56bm9HeVWgz2tbY1J5tXbGpMGjZBaeMiHjd4un3Gkg4N2iPBYYD34sIQB/gQ+BhoL/nJBHJAdzGmCIRyQ9W1pzKFRaWNkplGi1yczMoKChp+sQYoG2NTe3Z1qKDld7H0rcrJx3Vl0deX+k9lp7kjGjd4uX36nQ6gnaSmxzGMcbMNMb0MsYMMMYMALYDJwP/ADqJyDH2qVcDr9iPl4YoU0rFuVlvWIF95IAsbjh7NGOH5rZzjWJfi5eeGWNcwMXA4yKyAesTwB1NlSmlYteuwjK/HaYCcbvdfL/L6mVf/ePDSUu1Bhh0nL51NXtRld279zz+ChgV5LygZUqp2PTbJxcB8NRtx+F0Bl4UVVtXPzTrm+fmzovGc83988lMSwp0mTpEmlRCKRURvvnlb3jo86DnzQ+QmhggJSmB+66dzD1XTop43ZQGe+XD5XazZ395e1dDdVDrt9cP3/imHfZVXlnDJ99awf53M45sVN4tM5XOqdqzbw0a7JXX+wu3cufshey0F7oo1RwF+ytClh8sr+b6Bz9nT1E5WRkpDOqV2UY1U6DBXvlYtdmaGbu/VJeqq+bbVVRO/x4ZHDE4O2D5Bp8bt5lpyW1VLWXTrJfKy/PROynBSeGBSg6UVWvvS4Wt6GAlvbI7k90llZQGM3L+8+kGPlxcv2l4dW1dW1cv7mnPXnmVVVrBvq7Oxa2Pf8Vf/v0NX6/ajdsdnQvbVPSoc7nYVVhOVmYKCU5Ho8WQvoEeoFg/PbY5DfbKq9Be1VjnE9yf/O8alq7TzNQqtN/+y5pymZmWjNPpoK4udAehqtrVFtVSPjTYq0Y8Y/cei9fsbqeaqI5ib7F1c/bI4XlWz97txu12U1Pr4tePfel3bv8eGfzo6P6BXka1Ih2zV418tMT/I3dhcWWQM5WC1VvqOwedUxNJsBdTPf+h4YTxfSg6aA3Z/HT6YCYf3oOu6SntUs94p8FeAYRMOLersLQNa6I6kvLKGu5/eTlgrYbNSEv2bngxb/lOSivr59uPGZKjgb4d6TCOAuoTUwVSW6s3aGPNgdIqiiOwG9Tst9d4H99y3hgAxvkkNfvGvt9z+WkjdEOSdqbBXgH1Y66BuNFgH2tuevRLLr77g0N+Hd8FeP17WPni++Sl84dLJvid1697fOeSjwYa7BUAY4fm+D0/+ai+zLn9OI4Z3ZPdheVs2nmgnWqmIq2pqbQvf7KB1VuKqK5pei58akoCY4bkMMfeLNzDE/g9sjJ0+Ka9abBXABworSYlKcH7fFjfrjgcDvbZPf57/r20vaqmIqy0oiZo2bqt+/loyTbuf3k5v37sq5Cv43K52VFQRl5WJxyOwBkuAY4b21vz3UQBDfaK8spaPl+xC+nX1XssOdEK/L4paFXrqK1z8eQ7a8jf0zY7Ka3Lr1/d+spnG/3KHn59hfdxqDcFgJkvfgtAdmZqwPIHbziG8cNyOW3ygBbWVEWSBnvFi/9bD/hvFZeUaP3XyOnSqV3q1B4qq2s50MorO8sraxplhNy6u4SvV+/m+Q8Nn327nbe++L7Vvv/e/eU8/uYq7/MPFuX7lffJa7yByFJTwMHyar9j2/aWsnG7NbR3zOieAb9XZudkrjtrlA7hRImwpl6KyJvAQMAFlAI3GGOWi8gw4DkgGygEZhhjNtjXBC1T0cUT5H1nXyYkWB/LT58ygA8W59M1PbYTVxWXVnHzo9bin6fvOD7ir+9yu9m+t5S7n1nC6MHZ3HjOEd6yggPWUNnmnQfZtPMgAKdPHhB084+GPlqcT163NMYMyQl53o59Zfz+qUUBy6pr6njm/XVs3H6AIyWXb0wBYL0BznpjJf26p3P3pUd5z/cE/8QEJ51SdAZ3RxBuz/7nxpgjjDFjgfuAp+3jTwCzjDHDgFnAbJ9rQpWpKJSWmshTtx3Hr382hsG9ugDQKSWRU48eQF2UbvweKa3ZmwZ46eMN3P3MEgBWbCr0K/uXPX3R9yf82bLAG3w0VHSwkpc/3cjDr60Iek51TR2XzfyUP8xZ7D02fUwv7+M1W4q4+v75LFqzB4CkxARG9M8C4NoHFgCQv8d/rcVXK3cBcOdF48Kqp2p/YQV7Y4zvVIwugEtE8oBxwEv28ZeAcSKSG6osMtVWkeS5uXb1GYfhdDoYOaCbX3mnlEQqq2M7S6HvorJQC8xa6pNvtzc6Vlldy5z/rglwNrzw8fqwXtezOhUIOntm9turAevTBcAVp49kxinDOfcHwwC4z14U5XHEkGyKAszBr62rz2fz9WrrjSE1OaHReSo6hT1mLyJPiUg+cA/wc6AvsMMYUwdgf91pHw9VpqJMcWkVR0ou3YLcaOuUmkhNrcvvj72hguIKvzH/jsRzg9pjXpBt88ora9lsD7M0V+9ca0HRT6cPBuCLFbt4f2E+X66y8g6NHGD1pMf79IeC7fbk669z62dJffzNtkblKzYVsmzDPu/z5CQnRx/WA8Cb1qChQT0zOc6n5++x334DWLW5/pNJj25pTdZRRYewB9uMMZcDiMjFwD+A37dWpTyys6N7t/nc3NhYKHKgrJrxI7oHbU9elvUHXetw0jPAOSXl1dw+81MAXv7LD8nfXcKIgd0anReN1ufv55aHFvgdm/vRes47eUSjc2/65zw2bj/AW/84I+zxdI+szFS6ZqQyZnh3Xpu3iaffW+tX/scrJzN/2XaOGtmDr1ft4vHXV/DSpxtZvGYPd18+iVFNjMcDbN5V4vc7XLVpHw+++p3fOdU1Lu85vm144o4TSEpwsmF7MSOG5tG3d1c+W76TPUX121T+79sdXHnmKB54xXrNH0zoR15ex9nvIFb+Xluq2XdWjDHPi8i/gO1AbxFJMMbUiUgC0AvYBjhClIWtsLC0VT5SR0JubgYFBW0zVa417TtQQXllLSvWFwRtj2f14+oNBaQG+Czou4ryZ797D4Bfnj2aMUObDlDtyeV2+wX62b+ezlX3zQNg+drd9M7pTEl5Ne8vyufHUwZ6Z5/s2XuQxITmTWSrqqolwenAUec/1DKkTxeuPG0kJQcrGDc4m9qqGsYOst4ov/huJwC/efxL/nXr9IDfc0T/LNZvK6bO5Ub6dvX7HT7z9qpG5wPec3x79tUV1SR3SmJYz/r/13+zN/4ur6zh+gc/5+PF+Xy1Yqf3GrfL1WH+BmLl77UpTqcjaCe5yf+xIpIuIn19np8OFAF7geXA+XbR+cAyY0yBMSZoWYtboVrF2i37AcjuEngIB6CzPdd+UTNSHc9+e3XUvlF73PHE197Hj908zTvdFGB3odWjnbd8Jx8syudTnzH3lmzm4nK7cTod9MzuzJE+QzV3XjiOnK7+01sTE5yN1jc0nA/vW5e8LOv6lz/xn+w2TvK8jzM7N55NNeWI+qGaUOsp0nwWRJX5JDbTWTgdSzjdk87AqyKyUkSWAzcBpxtj3MDVwA0ish64wX7uEapMRYnnP7JuBF5+2sig53gWR34T5L26prbxWH5VTR3b9kZvtsyq6jr2HbDuMdx6/lhSk63A9bdrpwDQKcW68bhwtfUG9+q8Td5rWzIzye0Gp/2DnHHKcO/xYCtPuzWYm+4OcrvE5fbfzzXQG9E/r5/CXT8/0v5+9cdzu1rDcy2dBx9sfr2KTk2+NRtj9gCTgpStAyY2t0y1vdKKGtZsKeKoEd29xyqra6mtc9Erp3PYvbR9ByoaLbQy+dangyF9uniHOgDW5e/HjZsBPaJrXPdAWTWf2T31Uyb2804zBLxDJZ6AvquwPOD1njeHcLlcbm9g9wyfOEOkGPDsGnb+D4by0v820Ld74I/mbrebBJ/hnZ37yuidm263wXqHSElOINlOhXHmMQO95yYlOrn9grFhZaOcc/tx/OLvn3mfP37LsX7pNVT0089hceLeF5exvaCUXjmd6WMHgwNl1sKYUyf2C3mty6e3GChAvfypNcSQ1GBM+T/28dZYpNRStXUubnrkC+/zqQ16p57FZJ6ZRwlOhzfw9+ueTv6eUp5+dy13XjS+Wd/X5XbjGSJPSUqga3oyZx87OOj5N507hjc+38yRksdL/9tAZXUdLpfb76ZqTW0dG7Yf8M7kASgoriQhwcn6bcWUlNWQmOAkJSkBh8MR8Pcg/bIaHQvE4XDwuxlHsmNfKaMHZWug74A02MeBrbtL2F5gDakUl1R5g31puZX7pKn8N92z6qfXNRx28H0jGDcsl7Vb9ze6vrbO1ewbmq3lQKn/sv/cBuPlnqGNR16vz+8/5XBrquIPj+7Pb59cFHTKYvDvWUX+nlLvwiSn08ED1x8T8ppBvTK55bwxHLTfkF/+ZAMvf7KBq844jIkjrU9nL31ivZmu2VL/M3/ug3XeN3GAnC6pIZOUNcegXpkM6hVdn9JU+KLjL1C1qntfWuZ9XOsz3pxvj6mnp4UO9gkJTi491RpnvmXWlxT75I/xjHsDHD+uN3+87CgevXGaX0C8v8Ginfa0flux3/OGb0JdA4xfD+iZyS9OG0nP7M4M6JHBuvxiv59BKHUuFzfZaRgOGxBeL9pXwymes99ezarvrXnunjdwgJlXWSOtvoEeNLWwqqfBPoa53G5M/n6/xTnvfr2FR15fwWfLdvD8hwYIL7Olb9BZ7rNIx22/eVx+2ggcDgd989JJS01kWN/6DJqmQYBtTwV2yuaBPTO54exRAc/J7eo/M8l3TLvE/jR0a6fuAIAAABq7SURBVBPpfz3mLaufqnjF6Yc1q64QeIXqA//5jvcXbqXS5/eaGuSeiwZ75aHDODHskddW8J2dh2XkgCzWbNnPph3WClDfVZUNhzIC8Q32vkvpPcM4DXug1/1kFH/59zfsLmp8g7M9vb/YyvJ4+wVjvTctG5p51dEsXLOHYX26snbrfob7pH7+/SVHcuPDX1DnclNdUxf0NTy+XW/NYBrSu0vA6Y9NSUxwktk52Tuc433dDQXU1LlJcDq495rJpCQGrofu+ao8tGcfxRZ8t5O/Pr/UO52uuqaOFZv2hUxb4Os7n4RbN517RMBzLj11eMhZIR6pPkHtv19t8T72DNk3fI201ET+coU1GSuvayfeX7iVXz38eVj1bk1Vdo6fUEHa4XBw9GE9yO6SyjGje/qNeftOc3z6vbXemUge+XtKuOOJr715ajz3MA4lYdgD103hqduO495rjqZntnVPYdOOg+wpKufYMb3IykghKan+T3nO7ccx6TBrXL+z7kegbBrso9iz769j444D3ul/X67cxYOvruDKf8zjjQWbQ17bsCeY4HRy5tSBfsfOmjaIqUc0zoESSLBhAs8bUaCbgE6Hg145nenbPZ1X522ipLyGssrQG2K0FrfbzXMfrANg2hGRmR++eO1e/v7iMv74zBIqq60hlbufWcLe4grvIqi+eemMGpR9SDdJnU4HTqeDnC6duOeKSX6zpzwLnnzfbB0Oh7dHH5lbsyoWaLCPQi6XmyqfDIYfLbGyTGz3SUvwjk/vOpDvd1nDNRNHdmfWTdMAOGPKQG6/YKz3nObsINRwha1n+MbTsw8WVA6WVbPUZzGW73h/W1qxqZD5y63x8/LKphOMNcfWPSUsXL2Hy+z8QACffmslUztQWkVWRmT3AjhpQn0+wfTUwG/C/bvXb/6tFOiYfVS66r55fqs0F3y3k4Nl1SzfGDpQ7iosY+5H67nyjMPYam9x95OpA/0WTEm/LE6a0LfRBuNNyevaib9cPpGVmwv5z6cbKauoISMt2Rv0g/VcG25tN+fdtUwZ1fYrL323/PNNi9ASd1w4jpkvfOt37IPF+Y3O219SxcFy6+cUSb6zp3yHaX4wvo938dVRI/Lok9vZu8BKKQ32LbS7sIyqylrSgvSsDkWg5fiBAv1jb6zkR0cPoH+PDFZ/X8T9/7GmOPouGgp08/VnJwxtUb165XT2jkHvLionNTmRP9obckRoKnfEud1udhWWs2d/BclJTk6a0JeTJoReRNYU35lGHnv3V3gf987tzI6CMl6zUyz0jXDvOsFZ/2bVxeem7wUnDvM+djgcGuiVHx3GaYGKqlqu+Ov/uP7BBX6LiiLBM/brMfvX072JrgDOO36I9/E3poA3P9/M3v3l3kDfUKQW1Hiss4P93I/Ws3zjPu/uSs35NnuLK5o+KULeW7iV3z21iK9W7WbcsFzOmjY4Ipuoe/LCBFpJero9PPa1nVfHN0VFpNx96QSOGpEX9gpYpTTYt8Cq74u8jxsOUxwqz43V5EQn9107maREJ2k+wzDD+2Vx1Rn187W/21TIHbMXep9nZ9ZPtbvv2skRrRvA0fZqUqfD4bdxdbA3lb9dOcm7cOnEI62x5vlhbrl3qN7+4nten19/IzuSOXouOWU4j908jcdvOZafTBvkPd4pJdGvt91amSH7dc/g6h8ffshDUip+6P+UFvBdzFIbIOPjIb22PTXwitMP8+4c5bs5df8eGRw1Ii/gaswbzh7FP66dwhlTBtAvLz3gatBDNW5YLj26pXnvCXis2VIU8Pzu3dJ44pZjmXnVJM47wfpUsnbrftYGOT9SCooreLPBvrKjB2dH7PWdToc3GZrvJ4U/XHIkQ/vUD/PcfF7gKa9KtTUN9i1wsLx+WqNnW7mGduwrY+XmwoBloXjG6z0JucDKRf7YzdOYc/txgNWLvuVnY/1WV2ZnpjB2qJUn/cypg7j7sqPCmj/fEpkB0iuMCbGTktPpIC8rDafDQU6XVLbsLuEfLy+P6BDYE2+tYu5H1orgjdsPcLtPrnqwbqq21hZ6nk9eeV07We10OrzDWn103FxFCQ32zeB2u1m7dT/vL6yfefHGgs0Bg9bvn1rEP1/5rtHxpngW4yQ2WJGampzYaKjknOn1WROv/Ungpf+toeFm1PddO7nRJuXB+ObS+cJn39dDtXjtXj79dgcut9tvX9ZTjurHzecdEfCmaqR43vxyfe6tzLn9eJ6+43jNDqmiRpMDiiKSDTwPDAaqgI3AVcaYAhGZBMwGOgFbgIvsXaoIVdYRzfnvGjbtPBhw+f+y9QWM99kVyFedy+U3e+KLFbtYvaWIK04f2ajnXVFVy99ftJKWhZNZcdqYXhwxJCfoRuGtxXe3ogdvOKZFaQAACg9EZoNy33nzT7y12vs4KdHJuT43tFvLsH5dOe/4IWG/4SnVHsLp2buBe40xYowZDWwCZoqIA5gLXGeMGQYsAGYChCrrqL5ctTtonpe0BjfhfJfQF5fUD/kUHazk6ffWsmjNHpat959Kue9ABfN8blw2lXMFrCl4bR3oAcYPq99Wr7mB3veGZbfMyNxT2LyrfsOUb9bV9yfuuaJt9s5JcDo5+ah+EZ9iqVQkNRnsjTFFxph5PocWAv2BI4FKY4xnUvcTwLn241BlHU6gbff8yuvqh3Fqauu8vXOw8ot7Ugr43tTMb3CD87bHv/ZufTdpZHcG9ozevOFiJwbz3fUoXFNG9fA+fu4Dc8h1+XLlLhat2dPo+PQxvRrtqKVUPGvWmL2IOIFrgLeBfsBWT5kxZh/gFJFuTZR1OJ81mCo4+9fT+dV59WkHqn1SG+wp8p9Dvur7IjbbqQt8N8TwTXfgm4IY4MozDmuURTKaTD68B3+5fCJntCDY/+z4ofzl8sj0uJdv2Mecd9fy5crdpKUk+qURaOoNWql409xJwI8ApcCjwE8iXx1/2dnt/7F4d2EZL3+yAYCxw3L5xRmH06tnJk6fPUhT05LZc7AK6Z/F/QFuynZOT2Xmi982Ot6tW2ecTgfbfTbmPnlSf3JzM1qhJYemYZ3y8lr+ySMvr/61WtrWJWt28/DrK7zPy6tque7csfTpkcnT76xm7IjuLX7taPz5txZta/wIO9iLyH3AUOB0Y4xLRPKxhnM85TmA2xhTFKqsOZUrLCzFFSB1QFv6v3kbvY9vOMua8VJQUEKP3AweuH4KNz/6JY+99h2V1XX86Oj+rLanW/7tykm88flmFq/dywMvLPXOQpk6uicL1+yhptbFmbe9A9SPY1/6w+FMHd2LggL/IZ72lpub0Wp12rptf4tSTvxpziK/5zeeM5rCwlKmjMwjLzOFoX26tKjOrdnWaKNtjT1OpyNoJzmsYRwRuQcYD5xpjPHMu1sKdBIRz2aaVwOvhFHWoXhmenjyiPvyBGnPQqh3v95K37x0eud0pnu3NE6xU9H6Tjf88TED+d2MI/1exzOMc/jAyC366Siuf3ABW3cH/yPcV1zBtQ/M97vH4btqedoRvbjt/LGMHmzN83c4HAzr2zXiaSKU6uiaDPYichjwG6AX8JWILBeRN4wxLuBi4HER2QAcC9wBEKqsoyk8aAXqW88f26gs0Bzq8soaeuda29hlNdgl6I4Lx9EtMzXgrI0fHzMwrraQO8snxcAfn13C/pLAe7ouWLGLyuo6Fq2tvwn7y4fqN0G55NThDO+v+WGUakqTn5+NMasJkq7cGPMVEHA1T6iyjmTnvjImDM8Le3u3woNVHDfOGhvskp7Cz04YyoZtxZw+ZQD9utePGV544jBe+Hg9pxzVjx8e3T8iybk6kt4++7oC/G3uUu69pnEunzp7V673F+ZzzvQh3plNAL+dMb51K6lUDNEUxyEUl1ZRdLCK9NTg+6hmZ6Z6e/8evj16K6Vu34aXccL4Ppwwvk/kKtvBpDTYSHtfkAVWvmsbXC437y70TvJicK8urVM5pWKQpksI4Ws7782wfsGX2ntSDvuuhq0Jc4/YeNYw2AeyY1+Z38boz36wzrsd48UnDQt2mVIqAA32IVTYueXPnjY46DlD+3YlKyOFUyfVb4gR7pBPPEtN9v9Q2TAj5crNhWyx1yd4+ObSOWxgh1yyoVS70WGcEKprXKQkJYTshXbpnMz9102hqqaO7XtLGdAzM6KpdGNVwzzsnjw5hQcqKS6r8ksid97xQ/jPp/VTYPt1Tycvq3UyWCoVq+I+2Lvd7kbT9Eoranjv661s2V0SVkIysGbm/OoczV0ertwuqZwzfTC9c9N58NXvvBus3/r4V43OHTM0h+827mNdfjEA50xv/eRmSsWauA72Lreby//+GWcfO4gfHT0AgGUbCvzSGqjW4XA4OHWSte7umFE9+WLlrqBZMLtnpXHikX29wV5C3ENRSgUWt8H+3x+s826X9/r8zZw0oR9JiU4N9O1g004ra+U/XloW9JwSn4VUnt+bUip8cfdXs7+kis+W7WDe8p38b+l27/Gr7pvnN4f7qBGB89OryOtmLyYLtRH5hOH6+1DqUMRdz/6WWV8GLfvF3z8D4OxjB3HqpP70yU1nyqiebVW1uDVyYDdWb6nfA2DKqB4cNrAb/3p7jfeYJzXFkD46t16ploirnn3DtLe/uWg89183hf49/LPhTT68J06Hg9MmD4irFAbt5ZSj+vk9//kpw5k00sp777uy+IHrp3DLuWPatG5KxYq46tn7DhP06Jbm7SX+4ZIJ/PtDw7xlO/jrlZM0wLexhrOhPGPyD/9qKr6ToXT9glItF1fB/suV1qKc31w0vlEysotOHMaxR/SiRzedvx0t4i1fkFKtKS6Dff8eGY0W9TidjkbDOart/TlCu1gppfzFVbDP6ZJK96y0RoFetb9/3TqdrbtLGmXDVEpFRtxEvdo6FzsKyhjQU3vv0Sgxwcng3jrTRqnWEhc9+8rqWq59YAEAQzSgKKXiUJPB3t579mxgADDKGLPKPj4MeA7IBgqBGcaYDU2VtTWX2+0N9ABHDMlpj2oopVS7CmcY501gGrC1wfEngFnGmGHALGB2mGWt4kBpFSs2FTY6vnNfWX2lbjk24FaCSikV68LZlvALABHxHhORPGAccKJ96CXgURHJxdrCMGCZMaYgclW3LN+4j4L9Fbz0ifXBYfavjyUpsT6gl5RVA3D7BWNJ1kCvlIpTLb1B2xfYYYypA7C/7rSPhyprsaqaOv7x0jJ2Fdb31DfvPMjDr63wBnqAq+6bz8btVmKtyupann5vHaBztpVS8S2qb9BmZ9cvfLp65v/YUVDGX+d+y9O/O5G01CQum/lpwOv+Oncpt1w4nk3bi737ww4blENaamQDfm5u/Mzs0bbGJm1r/GhpsN8G9BaRBGNMnYgkAL3s444QZc1SWFiKy2VlotxRYPXoyypqOO+37zV57f0vLPU+TnA6KCuppKwkcL70lsjNzaCgoCRirxfNtK2xSdsae5xOh18n2a+sJS9ojNkLLAfOtw+dDywzxhSEKmvJ92rK/ddN4dbzxwLWuPyc24+jS+dkb/mlPxzOozdNa41vrZRSHUY4Uy8fBs4CegD/E5FCY8xhwNXAcyJyF7AfmOFzWaiyFsnsnMxB+2arr6yMFLIyUnj85mO9e8XOvPpo3l+4lTOOGYjTEd62gkopFcvCmY3zS+CXAY6vAwImMglV1lK1tS5OGNeHQb0y2bC9mItOEpw+KRF9NwVPSUrgzKmDIvntlVKqQ4vqG7Qe+4orKK+qZVdRGReeNIyjD+/R3lVSSqkOJepz45RW1HDbE18DsMNngZRSSqnwRX2w/9OzS7yPf3vx+HasiVJKdVxRPYxTWV3HvgPWdMmHfzVVF0YppVQLRXXP/tn31wJw5PA8DfRKKXUIojrYb955EIBLTpEmzlRKKRVKVAd7sMbpI53mQCml4k1UB/szpw7S3YuUUioCojrYT9b59EopFRFRHeyVUkpFhgZ7pZSKAxrslVIqDmiwV0qpOKDBXiml4oAGe6WUigPRmhsnAfDLVx+Nor1+kaRtjU3a1tji08aEhmUOt9vdtrUJzzHA5+1dCaWU6qCmAl/4HojWYJ8CTAB2AXXtXBellOooEoCewBKgyrcgWoO9UkqpCNIbtEopFQc02CulVBzQYK+UUnFAg71SSsUBDfZKKRUHNNgrpVQc0GCv4o6IxP5SSlu8tDVe2nkoNNiHICJniEi39q5HWxCRHBFJsB/H+h+Odyl5rLfVGBMvC2nSAUREY1oQ0Zobp12JyDnAn4BPgWVAUfvWqPWIyAXAr4ENQBlwWawGCBGZAVwHfCMiy40xT8ZwWy8ELgS+AxYYY95v5yq1ChHpg5VaZS3wQyAmf5+RoO+CDYhIf+Bq4GpjzHXGmG3tXafWIiKXA1cBNwN/ACaKyOj2rVXrEJEfAL8CbgM+Am4VkSvtspjp3YtInoi8DVwG3If1N/6LWP29Ai6sjspJInKCMcatvfvA9IfS2GRgjTFmvoj0FJELRWREe1eqlZwEPGuMmYeVj2gVsLtda9R6fgC8ZoyZb4x5C7gRuEdEsmOsd98ZmAecZIz5FHgGa4gjZt7QGhgEvIXVQZsDYIxxtWuNolTcB3sRuUxEpohId/uQC5giIscDbwAnAM+JyKP2+R32Zxagrd8AM0XkceBtrKDwmog8Yp/fkduaZX9Nsg8Z4FRPuTHmPeBb4B77vA7fVtse4GljTJ2IJBpj1gHZxMDwhs/v1Pd3VQ4cZYx5CnCIyA0iMlVEUtqlklEsbhOh2R9rXwK2AzuB/lhjfp2AJ4FM4AZjjBGRIcBqYKgxJr+dqtxiAdraDzjNGFMhIlOwxuzvNMasE5HBWIFxsDFma7tVuoVE5DRgLrDBGDPB5/gIYCbwgjHmFfvYSOADYLQxprg96nsogrW1wTljgEeA440xNW1Zv0gJ1U4RuQZIMsY8LCIzsYbp3gPOBOpi7FPbIemwvZmWaDA2OxGYa4w5GWvcugRrSGM/Vo93GnAAwBizEes/UO+2rXHLhdHWuXbZ91jtygcwxmzCCoD92662kWF/YrkcuAHoLyI/8ynegXXDfYaIpNnHyoD5QBIdTLC2enq9Pr//acAqY0yNiIwUkVMDvmCUCtFOz+9sO3CciHwIHAcsB7YbY2qJ3aGrFombYC8iyQ3e5acCfQCMMdXAz4GTRWQy8CDwPtYQR56IPABkAWvauNotEkZbLwGOF5FjsT72bwH+JCJOEfkn0AVr/D7qiUiy57ExZg9wtzHmeeBO4CGfsoPAm8BBYK6I9LPP6QJ0iF59OG31Ga/2/G33BHaKyO+A54E0olyY7fR8SukCdAfeN8ZMBI4HLhCRHB279xcXwV5EbgS+EpF7ReQS+/BLwGjP+LX9Mf5R4D5jTCXWtLW9WEM6SVjDHgfavPLN1Iy2PgLcY4ypA2YBY4GFWG091RgT9dNNG7T1YvvwSgBjzBxgn4j8xT7XaQ9LXYHVy38Ka779+R1heKOZbU2wx+wdwMXA9VhB/lhjzOvtUP2wNaedtjeB44wxD9rnFAM5xph9bVnvjiDmx+ztudVXYI1LDwbuxwrky4B7sT7y/dE+dwDW7IXLjDHf28fSjDHl7VD1ZmtBW58FLjbGbBORTCDVGLO3HarebEHaOsMY87H9yaZaRI4GFgDpxpgqEck1xhTYQTDNGFPWfi0IXwvbmm2MKRSRu4A3jDEr268F4TnEdiYDNTpGH1xM9uxFJMPn6QnA48aYRcaYF4G/YvVky7GmbJ0iIifY5x4ObPMEeoBoD/SH2NZ8zzoCY8zBaA/0TbT1HuAxuwdfLSIpxpivgZeBD0XkFeB3YK0qjfZAf4htfRX4PYAx5k/RHOgj8Du9C6zhSQ30ocVUsBeRRPsj3tsi8mcROQxYDPzUc44x5hH74TXGmP8CrwAPi8jzwAM02KQ3WmlbG7X1UaAauMU+VG1/LQeOBlYaY37VhtVukQi1dYUx5sY2rHazxdPvNFrETLC3P959gzVl8k6sO/EPAOuARPtmpMdtWKsKHcaYfwJnAx8CU4wx/2rbmjeftjVoW+8AZoDVexeRO4FUoLcx5s9tWvEWiJe2xks7o00s5capxvoIOBusm1TAMKzpk98C12JNswNrlehCIE1Eyu2FJ+vavsotpm0N0VYRSTfGlAL327OPOop4aWu8tDOqxEzPHliBNaXOM7e2BBgALAVeALJF5FERGQTcDriNMWUddJxP2xq6raXgnWbakcRLW+OlnVElZnr29vQ53yl0RwLr7QC3QUR+gXWXfy5WFsA72qGaEaFt1bbSgdsaL+2MNrHUswe8HwkBxmN/FBSRn2JN1boB+EGs/OfRtmpbO7J4aWe0iMl59vbHw1ewZptMBPKAm6J5ClpLaVu1rR1ZvLQzGsTMME4DgjXrpC/wpL3yLlZpW2NTvLQ1XtrZ7mI12O8EfgP80xhT1d6VaWXa1tgUL22Nl3a2u5gcxlFKKeUv5m7QKqWUakyDvVJKxQEN9kopFQc02CulVBzQYK+UUnFAg71SSsWBWJ1nr1RYRGQL1h6mtUAd1j7D/wb+ZZrYw9Te7et7IMlYG1wrFbW0Z68UnG6MyQD6AzOxMi3qSk4VU7Rnr5TNWBvKvy0inhzq92O9AfwFa0/UA8AcY8zd9iUL7K/FIgJwojHmaxG5DLgV6IG1+9KVxtrsXKl2oz17pRowxiwGtgNTgTKsnZK6Aj8CrhGRM+1Tp9lfuxpj0u1AfybW8v+zgFzgc+Cltqy/UoFosFcqsJ1AN2PMPGPMSmOMyxizAitwHxviuquAvxlj1trj+H8FxohI/zaos1JB6TCOUoH1BopEZCLWOP7hQDKQArwa4rr+wEP2EJCHw349HcpR7UaDvVINiMgErOD8BfAm8ChwqjGmUkQeBHLsUwNlEdwG3GOMeaFNKqtUmHQYRymbiGSKyGnAy8BcewONDKDIDvRHARf4XFIAuIBBPseeAO4UkcPs1+wiIue0TQuUCk6DvVLwjoiUYPXKfws8AFxql10L/MkuvwtrVyUAjDHlwD3AlyJSLCKTjDFvAH8HXhaRg8Aq4NS2a4pSgWk+e6WUigPas1dKqTigwV4ppeKABnullIoDGuyVUioOaLBXSqk4oMFeKaXigAZ7pZSKAxrslVIqDmiwV0qpOPD/L6y2P4Sy9ZcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "goog.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resampling and converting frequencies\n",
    "\n",
    "### 重新采样和改变频率\n",
    "\n",
    "> One common need for time series data is resampling at a higher or lower frequency.\n",
    "This can be done using the ``resample()`` method, or the much simpler ``asfreq()`` method.\n",
    "The primary difference between the two is that ``resample()`` is fundamentally a *data aggregation*, while ``asfreq()`` is fundamentally a *data selection*.\n",
    "\n",
    "对于时间序列数据来说有一个很普遍的需求是对数据根据更高或更低的频率进行重新取样。这可以通过`resample()`方法或更简单的`asfreq()`方法来实现。两者的主要区别在于`resample()`主要进行*数据聚合*操作，而`asfreq()`方法主要进行*数据选择*操作。\n",
    "\n",
    "> Taking a look at the Google closing price, let's compare what the two return when we down-sample the data.\n",
    "Here we will resample the data at the end of business year:\n",
    "\n",
    "观察一下谷歌的收市价，让我们来比较一下使用两者对数据进行更低频率来采样的情况。下面我们对数据进行每个工作日年度进行重新取样："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "goog.plot(alpha=0.5, style='-')\n",
    "goog.resample('BA').mean().plot(style=':')\n",
    "goog.asfreq('BA').plot(style='--');\n",
    "plt.legend(['input', 'resample', 'asfreq'],\n",
    "           loc='upper left');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Notice the difference: at each point, ``resample`` reports the *average of the previous year*, while ``asfreq`` reports the *value at the end of the year*.\n",
    "\n",
    "注意这里的区别：在每个点，`resample`返回了*这一个年度*的平均值，而`asfreq`返回了*年末的收市值*。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> For up-sampling, ``resample()`` and ``asfreq()`` are largely equivalent, though resample has many more options available.\n",
    "In this case, the default for both methods is to leave the up-sampled points empty, that is, filled with NA values.\n",
    "Just as with the ``pd.fillna()`` function discussed previously, ``asfreq()`` accepts a ``method`` argument to specify how values are imputed.\n",
    "Here, we will resample the business day data at a daily frequency (i.e., including weekends):\n",
    "\n",
    "对于采用更高频率的取样来说，`resample()`和`asfreq()`方法大体上是相同的，虽然resample有着更多的参数。在这个例子中，默认的方式是将更高频率的采样点填充为空值，即NA值。就像之前介绍过的`pd.fillna()`函数那样，`asfreq()`方法接受一个`method`参数来指定值以那种方式插入。下面，我们将原本数据的工作日频率扩张为自然日频率（即包括周末）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, sharex=True)\n",
    "data = goog.iloc[:10]\n",
    "\n",
    "data.asfreq('D').plot(ax=ax[0], marker='o')\n",
    "\n",
    "data.asfreq('D', method='bfill').plot(ax=ax[1], style='-o')\n",
    "data.asfreq('D', method='ffill').plot(ax=ax[1], style='--o')\n",
    "ax[1].legend([\"back-fill\", \"forward-fill\"]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The top panel is the default: non-business days are left as NA values and do not appear on the plot.\n",
    "The bottom panel shows the differences between two strategies for filling the gaps: forward-filling and backward-filling.\n",
    "\n",
    "上面的子图表是默认的：非工作日的数据点被填充为NA值，因此在图中没有显示。下面的子图表展示了两种不同填充方法的差别：前向填充和后向填充。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time-shifts\n",
    "\n",
    "### 时间移动\n",
    "\n",
    "> Another common time series-specific operation is shifting of data in time.\n",
    "Pandas has two closely related methods for computing this: ``shift()`` and ``tshift()``\n",
    "In short, the difference between them is that ``shift()`` *shifts the data*, while ``tshift()`` *shifts the index*.\n",
    "In both cases, the shift is specified in multiples of the frequency.\n",
    "\n",
    "另一个普遍的时间序列相关操作是移动时间。Pandas有两个很接近的方法来实现时间的移动：`shift()`和`tshift`。简单来说，`shift()`*移动的是数据*，而`tshift()`*移动的是时间索引*。两个方法使用的移动参数都是当前频率的倍数。\n",
    "\n",
    "> Here we will both ``shift()`` and ``tshift()`` by 900 days; \n",
    "\n",
    "下面我们使用`shift()`和`tshift()`方法将数据和时间索引移动900天："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(3, sharey=True)\n",
    "\n",
    "# 在数据上应用一个频率\n",
    "goog = goog.asfreq('D', method='pad')\n",
    "\n",
    "goog.plot(ax=ax[0]) # 画出原图\n",
    "goog.shift(900).plot(ax=ax[1]) # 数据移动900天\n",
    "goog.tshift(900).plot(ax=ax[2]) # 时间移动900天\n",
    "\n",
    "# 图例和标签\n",
    "local_max = pd.to_datetime('2007-11-05')\n",
    "offset = pd.Timedelta(900, 'D')\n",
    "\n",
    "ax[0].legend(['input'], loc=2)\n",
    "ax[0].get_xticklabels()[2].set(weight='heavy', color='red')\n",
    "ax[0].axvline(local_max, alpha=0.3, color='red')\n",
    "\n",
    "ax[1].legend(['shift(900)'], loc=2)\n",
    "ax[1].get_xticklabels()[2].set(weight='heavy', color='red')\n",
    "ax[1].axvline(local_max + offset, alpha=0.3, color='red')\n",
    "\n",
    "ax[2].legend(['tshift(900)'], loc=2)\n",
    "ax[2].get_xticklabels()[1].set(weight='heavy', color='red')\n",
    "ax[2].axvline(local_max + offset, alpha=0.3, color='red');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> We see here that ``shift(900)`` shifts the *data* by 900 days, pushing some of it off the end of the graph (and leaving NA values at the other end), while ``tshift(900)`` shifts the *index values* by 900 days.\n",
    "\n",
    "上例中，我们看到`shift(900)`将*数据*向前移动了900天，导致部分数据都超过了图表的右侧范围（左侧新出现的值被填充为NA值），而`tshift(900)`将*时间*向后移动了900天。\n",
    "\n",
    "> A common context for this type of shift is in computing differences over time. For example, we use shifted values to compute the one-year return on investment for Google stock over the course of the dataset:\n",
    "\n",
    "这种时间移动的常见应用场景是计算同比时间段的差值。例如，我们可以将数据时间向前移动365天来计算谷歌股票的年投资回报率："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ROI = 100 * (goog.tshift(-365) / goog - 1)\n",
    "ROI.plot()\n",
    "plt.ylabel('% Return on Investment');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> This helps us to see the overall trend in Google stock: thus far, the most profitable times to invest in Google have been (unsurprisingly, in retrospect) shortly after its IPO, and in the middle of the 2009 recession.\n",
    "\n",
    "这帮助我们看到谷歌股票的整体趋势：直到目前为止，投资谷歌股票回报最高的时期（完全不令人惊讶）是IPO之后的短暂时期以及2009中期经济衰退的时期。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rolling windows\n",
    "\n",
    "### 滚动窗口\n",
    "\n",
    "> Rolling statistics are a third type of time series-specific operation implemented by Pandas.\n",
    "These can be accomplished via the ``rolling()`` attribute of ``Series`` and ``DataFrame`` objects, which returns a view similar to what we saw with the ``groupby`` operation (see [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb)).\n",
    "This rolling view makes available a number of aggregation operations by default.\n",
    "\n",
    "滚动窗口统计是第三种Pandas时间序列相关的普遍操作。这个统计任务可以通过`Series`和`DataFrame`对象的`rolling()`方法来实现，这个方法的返回值类似与我们之前看到的`groupby`操作（参见[聚合与分组](03.08-Aggregation-and-Grouping.ipynb)）。在该滚动窗口视图上可以进行一系列的聚合操作。\n",
    "\n",
    "> For example, here is the one-year centered rolling mean and standard deviation of the Google stock prices:\n",
    "\n",
    "例如，下面是对谷歌股票价格在365个记录中居中求平均值和标准差的结果："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rolling = goog.rolling(365, center=True) # 对365个交易日的收市价进行滚动窗口居中\n",
    "\n",
    "data = pd.DataFrame({'input': goog,\n",
    "                     'one-year rolling_mean': rolling.mean(), # 平均值Series\n",
    "                     'one-year rolling_std': rolling.std()}) # 标准差Series\n",
    "ax = data.plot(style=['-', '--', ':'])\n",
    "ax.lines[0].set_alpha(0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> As with group-by operations, the ``aggregate()`` and ``apply()`` methods can be used for custom rolling computations.\n",
    "\n",
    "和groupby操作一样，`aggregate()`和`apply()`方法可以在滚动窗口上实现自定义的统计计算。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Where to Learn More\n",
    "\n",
    "## 更多学习资源\n",
    "\n",
    "> This section has provided only a brief summary of some of the most essential features of time series tools provided by Pandas; for a more complete discussion, you can refer to the [\"Time Series/Date\" section](http://pandas.pydata.org/pandas-docs/stable/timeseries.html) of the Pandas online documentation.\n",
    "\n",
    "本节只是简要的介绍了Pandas提供的时间序列工具中最关键的特性；需要完整的内容介绍，你可以访问Pandas在线文档的[\"时间序列/日期\"章节](http://pandas.pydata.org/pandas-docs/stable/timeseries.html)。\n",
    "\n",
    "> Another excellent resource is the textbook [Python for Data Analysis](http://shop.oreilly.com/product/0636920023784.do) by Wes McKinney (OReilly, 2012).\n",
    "Although it is now a few years old, it is an invaluable resource on the use of Pandas.\n",
    "In particular, this book emphasizes time series tools in the context of business and finance, and focuses much more on particular details of business calendars, time zones, and related topics.\n",
    "\n",
    "还有一个很棒的资源是[Python for Data Analysis](http://shop.oreilly.com/product/0636920023784.do)教科书，作者Wes McKinney (OReilly, 2012)。虽然已经出版了好几年，这本书仍然是Pandas使用的非常有价值的资源。特别是书中着重介绍在商业和金融领域中使用时间序列相关工具的内容，还有许多对商业日历，时区等相关主题的讨论。\n",
    "\n",
    "> As always, you can also use the IPython help functionality to explore and try further options available to the functions and methods discussed here. I find this often is the best way to learn a new Python tool.\n",
    "\n",
    "当然别忘了，你可以使用IPython的帮助和文档功能来学习和尝试这些工具方法的不同参数。这通常是学习Python工具最佳实践。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example: Visualizing Seattle Bicycle Counts\n",
    "\n",
    "## 例子：西雅图自行车统计可视化\n",
    "\n",
    "> As a more involved example of working with some time series data, let's take a look at bicycle counts on Seattle's [Fremont Bridge](http://www.openstreetmap.org/#map=17/47.64813/-122.34965).\n",
    "This data comes from an automated bicycle counter, installed in late 2012, which has inductive sensors on the east and west sidewalks of the bridge.\n",
    "The hourly bicycle counts can be downloaded from http://data.seattle.gov/; here is the [direct link to the dataset](https://data.seattle.gov/Transportation/Fremont-Bridge-Hourly-Bicycle-Counts-by-Month-Octo/65db-xm6k).\n",
    "\n",
    "最后作为一个更深入的处理时间序列数据例子，我们来看一下西雅图费利蒙桥的自行车数量统计。该数据集来源自一个自动自行车的计数器，在2012年末安装上线，它们能够感应到桥上东西双向通过的自行车并进行计数。按照小时频率采样的自行车数量计数数据集可以在[这个链接处](https://data.seattle.gov/Transportation/Fremont-Bridge-Hourly-Bicycle-Counts-by-Month-Octo/65db-xm6k)直接下载。\n",
    "\n",
    "> As of summer 2016, the CSV can be downloaded as follows:\n",
    "\n",
    "2016年夏天的数据可以使用下面的命令下载："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !curl -o FremontBridge.csv https://data.seattle.gov/api/views/65db-xm6k/rows.csv?accessType=DOWNLOAD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Once this dataset is downloaded, we can use Pandas to read the CSV output into a ``DataFrame``.\n",
    "We will specify that we want the Date as an index, and we want these dates to be automatically parsed:\n",
    "\n",
    "下载了数据集后，我们就可以用Pandas将CSV文件的内容导入成`DataFrame`对象。我们指定使用日期作为行索引，还可以通过`parse_dates`参数要求Pandas自动帮我们转换日期时间格式："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fremont Bridge Total</th>\n",
       "      <th>Fremont Bridge East Sidewalk</th>\n",
       "      <th>Fremont Bridge West Sidewalk</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-10-03 00:00:00</th>\n",
       "      <td>13.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-03 01:00:00</th>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-03 02:00:00</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-03 03:00:00</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-03 04:00:00</th>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Fremont Bridge Total  Fremont Bridge East Sidewalk  \\\n",
       "Date                                                                      \n",
       "2012-10-03 00:00:00                  13.0                           4.0   \n",
       "2012-10-03 01:00:00                  10.0                           4.0   \n",
       "2012-10-03 02:00:00                   2.0                           1.0   \n",
       "2012-10-03 03:00:00                   5.0                           2.0   \n",
       "2012-10-03 04:00:00                   7.0                           6.0   \n",
       "\n",
       "                     Fremont Bridge West Sidewalk  \n",
       "Date                                               \n",
       "2012-10-03 00:00:00                           9.0  \n",
       "2012-10-03 01:00:00                           6.0  \n",
       "2012-10-03 02:00:00                           1.0  \n",
       "2012-10-03 03:00:00                           3.0  \n",
       "2012-10-03 04:00:00                           1.0  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('data/FremontBridge.csv', index_col='Date', parse_dates=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> For convenience, we'll further process this dataset by shortening the column names and adding a \"Total\" column:\n",
    "\n",
    "为了简单，我们将这个数据集的列名改的简短些，并增加总计“Total”列：\n",
    "\n",
    "译者注：最新下载的数据集自带Total列，因此，只需要缩短列名即可，下面的代码译者注释了原来的代码，并使用一行代码将列名缩短。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data.columns = ['West', 'East']\n",
    "# data['Total'] = data.eval('West + East')\n",
    "data.columns = ['Total', 'East', 'West']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Now let's take a look at the summary statistics for this data:\n",
    "\n",
    "现在我们来看看这个数据集的总体情况："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total</th>\n",
       "      <th>East</th>\n",
       "      <th>West</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10771.000000</td>\n",
       "      <td>10771.000000</td>\n",
       "      <td>10771.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>99.713861</td>\n",
       "      <td>51.416489</td>\n",
       "      <td>48.297373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>120.397155</td>\n",
       "      <td>63.867062</td>\n",
       "      <td>67.568734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>57.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>26.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>134.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>60.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>831.000000</td>\n",
       "      <td>626.000000</td>\n",
       "      <td>593.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Total          East          West\n",
       "count  10771.000000  10771.000000  10771.000000\n",
       "mean      99.713861     51.416489     48.297373\n",
       "std      120.397155     63.867062     67.568734\n",
       "min        0.000000      0.000000      0.000000\n",
       "25%       15.000000      7.000000      7.000000\n",
       "50%       57.000000     29.000000     26.000000\n",
       "75%      134.000000     69.000000     60.000000\n",
       "max      831.000000    626.000000    593.000000"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dropna().describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the data\n",
    "\n",
    "### 可视化数据\n",
    "\n",
    "> We can gain some insight into the dataset by visualizing it.\n",
    "Let's start by plotting the raw data:\n",
    "\n",
    "我们可以通过将数据可视化成图表来更好的观察分析数据集。首先我们来展示原始数据图表："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import seaborn; seaborn.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot()\n",
    "plt.ylabel('Hourly Bicycle Count');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The ~25,000 hourly samples are far too dense for us to make much sense of.\n",
    "We can gain more insight by resampling the data to a coarser grid.\n",
    "Let's resample by week:\n",
    "\n",
    "约25000小时的样本数据画在图中非常拥挤，我们很观察到什么有意义的结果。我们可以通过重新取样，降低频率来获得更粗颗粒度的图像。如下面按照每周来重新取样："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "weekly = data.resample('W').sum()\n",
    "weekly.plot(style=[':', '--', '-'])\n",
    "plt.ylabel('Weekly bicycle count');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> This shows us some interesting seasonal trends: as you might expect, people bicycle more in the summer than in the winter, and even within a particular season the bicycle use varies from week to week (likely dependent on weather; see [In Depth: Linear Regression](05.06-Linear-Regression.ipynb) where we explore this further).\n",
    "\n",
    "上图向我们展示非常有趣的季节性趋势：你应该已经预料到，人们在夏季会比冬季更多的骑自行车，即使在一个季节中，每周自行车的数量也有很大起伏（这主要是由于天气造成的；我们会在[深入：线性回归](05.06-Linear-Regression.ipynb)中会更加深入的讨论）。\n",
    "\n",
    "> Another way that comes in handy for aggregating the data is to use a rolling mean, utilizing the ``pd.rolling_mean()`` function.\n",
    "Here we'll do a 30 day rolling mean of our data, making sure to center the window:\n",
    "\n",
    "还有一个很方便的聚合操作就是滚动平均值，使用`pd.rolling_mean()`函数。下面我们进行30天的滚动平均，窗口居中进行统计："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "daily = data.resample('D').sum()\n",
    "daily.rolling(30, center=True).sum().plot(style=[':', '--', '-'])\n",
    "plt.ylabel('mean hourly count');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The jaggedness of the result is due to the hard cutoff of the window.\n",
    "We can get a smoother version of a rolling mean using a window function–for example, a Gaussian window.\n",
    "The following code specifies both the width of the window (we chose 50 days) and the width of the Gaussian within the window (we chose 10 days):\n",
    "\n",
    "上图结果中的锯齿图案产生的原因是窗口边缘的硬切割造成的。我们可以使用不同的窗口类型来获得更加平滑的结果，例如高斯窗口。下面的代码制定了窗口的宽度（50天）和窗口内的高斯宽度（10天）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "daily.rolling(50, center=True,\n",
    "              win_type='gaussian').sum(std=10).plot(style=[':', '--', '-']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Digging into the data\n",
    "\n",
    "### 挖掘数据\n",
    "\n",
    "> While these smoothed data views are useful to get an idea of the general trend in the data, they hide much of the interesting structure.\n",
    "For example, we might want to look at the average traffic as a function of the time of day.\n",
    "We can do this using the GroupBy functionality discussed in [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb):\n",
    "\n",
    "虽然上面的光滑折线图展示了大体的数据趋势情况，但是很多有趣的结构依然没有展现出来。例如，我们希望对每天不同时段的平均交通情况进行统计，我们可以使用[聚合与分组](03.08-Aggregation-and-Grouping.ipynb)中介绍过的GroupBy功能："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "by_time = data.groupby(data.index.time).mean()\n",
    "hourly_ticks = 4 * 60 * 60 * np.arange(6) # 将24小时分为每4个小时一段展示\n",
    "by_time.plot(xticks=hourly_ticks, style=[':', '--', '-']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The hourly traffic is a strongly bimodal distribution, with peaks around 8:00 in the morning and 5:00 in the evening.\n",
    "This is likely evidence of a strong component of commuter traffic crossing the bridge.\n",
    "This is further evidenced by the differences between the western sidewalk (generally used going toward downtown Seattle), which peaks more strongly in the morning, and the eastern sidewalk (generally used going away from downtown Seattle), which peaks more strongly in the evening.\n",
    "\n",
    "小时交通数据图展现了明显的双峰构造，峰值大约出现在早上8:00和下午5:00。这显然就是大桥在通勤时间交通繁忙的最好证据。再注意到东西双向峰值不同，证明了早上通勤时间多数的交通流量是从东至西（往西雅图城中心方向），而下午通勤时间多数的交通流量是从西至东（离开西雅图城中心方向）。\n",
    "\n",
    "> We also might be curious about how things change based on the day of the week. Again, we can do this with a simple groupby:\n",
    "\n",
    "我们可能也会很好奇一周中每天的平均交通情况。当然，还是通过简单的GroupBy就能实现："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "by_weekday = data.groupby(data.index.dayofweek).mean()\n",
    "by_weekday.index = ['Mon', 'Tues', 'Wed', 'Thurs', 'Fri', 'Sat', 'Sun']\n",
    "by_weekday.plot(style=[':', '--', '-']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> This shows a strong distinction between weekday and weekend totals, with around twice as many average riders crossing the bridge on Monday through Friday than on Saturday and Sunday.\n",
    "\n",
    "上图清晰的展示了工作日和休息日的区别，周一到周五的流量基本上达到周六日的两倍。\n",
    "\n",
    "> With this in mind, let's do a compound GroupBy and look at the hourly trend on weekdays versus weekends.\n",
    "We'll start by grouping by both a flag marking the weekend, and the time of day:\n",
    "\n",
    "有了上面两个分析的基础，让我们来进行一个更加复杂的分组查看工作日和休息日按照小时交通流量的情况。我们首先使用`np.where`将工作日和休息日分开："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "weekend = np.where(data.index.weekday < 5, 'Weekday', 'Weekend')\n",
    "by_time = data.groupby([weekend, data.index.time]).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Now we'll use some of the Matplotlib tools described in [Multiple Subplots](04.08-Multiple-Subplots.ipynb) to plot two panels side by side:\n",
    "\n",
    "然后我们使用将在[多个子图表](04.08-Multiple-Subplots.ipynb)中介绍的方法将两个子图表并排展示：\n",
    "\n",
    "译者注：因为`DataFrame.ix`已经不推荐使用，因此下面代码中的索引符改成了loc。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig, ax = plt.subplots(1, 2, figsize=(14, 5))\n",
    "by_time.loc['Weekday'].plot(ax=ax[0], title='Weekdays',\n",
    "                           xticks=hourly_ticks, style=[':', '--', '-'])\n",
    "by_time.loc['Weekend'].plot(ax=ax[1], title='Weekends',\n",
    "                           xticks=hourly_ticks, style=[':', '--', '-']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The result is very interesting: we see a bimodal commute pattern during the work week, and a unimodal recreational pattern during the weekends.\n",
    "It would be interesting to dig through this data in more detail, and examine the effect of weather, temperature, time of year, and other factors on people's commuting patterns; for further discussion, see my blog post [\"Is Seattle Really Seeing an Uptick In Cycling?\"](https://jakevdp.github.io/blog/2014/06/10/is-seattle-really-seeing-an-uptick-in-cycling/), which uses a subset of this data.\n",
    "We will also revisit this dataset in the context of modeling in [In Depth: Linear Regression](05.06-Linear-Regression.ipynb).\n",
    "\n",
    "这个结果非常有趣：我们可以在工作日看到明显的双峰构造，但是在休息日就只能看到一个峰。如果我们继续挖掘下去，这个数据集还有更多有趣的结构可以被发现，可以分析天气、气温、每年的不同时间以及其他因素是如何影响居民的通勤方式的；要深入讨论，可以参见作者的博客文章[\"Is Seattle Really Seeing an Uptick In Cycling?\"](https://jakevdp.github.io/blog/2014/06/10/is-seattle-really-seeing-an-uptick-in-cycling/)，里面使用了这个数据集的子集。我们也会在[深入：线性回归](05.06-Linear-Regression.ipynb)小节中再次遇到这个数据集。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [向量化的字符串操作](03.10-Working-With-Strings.ipynb) | [目录](Index.ipynb) | [高性能Pandas: eval() 和 query()](03.12-Performance-Eval-and-Query.ipynb) >\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/wangyingsm/Python-Data-Science-Handbook/blob/master/notebooks/03.11-Working-with-Time-Series.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
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  }
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